MicroRNAs (miRNAs) constitute a vast family of very small molecules. In humans, their number is currently estimated as more than 1000 and their size is typically between 17 and 25 nucleotides. The majority of microRNAs are subjected to two types of regulations: firstly, a temporal regulation and, secondly, a spatial regulation. The temporal regulation depends on cell, tissue and organism growth, differentiation and development steps. The spatial regulation reflects, for its part, the fact that microRNAs are specifically expressed in a genus, a tissue type or a cell type (Reinhart, 2000). The spatial regulation can be determined by an antisense capacity of the microRNA.
The antisense capacity allows the miRNA to exert one or more specific biological phenomena by virtue of more or less extended base pairings (complementarity) with predetermined regions of respective target molecules. These target molecules and these regions constitute what is commonly referred to as target messenger RNAs (mRNAs) and target recognition sequences (TRSs), respectively. Depending, in particular, on the location and the type of complementarity (complete or partial), microRNAs will lead to either a repression of the translation of the mRNAs, or a destruction of the latter. Thus, microRNAs can control the expression of proteins produced from messenger RNAs (mRNAs).
As a result, miRNA have the ability to determine how, when and where genes must be expressed and also the ability to coordinate the interactions between these genes. Even more generally, microRNAs orchestrate numerous aspects of cell and organism development and function, and play key roles in the regulation of fundamental cell mechanisms (Brennecke et al., 2005; Chen et al., 2004; Esau et al., 2004; Yekta S. et al., 2004). Unfortunately, in animals in general and humans in particular, it is not known, except for a few rare exceptions, which biological phenomenon (functions) are associated with which microRNA. Therefore, an important step today is identifying which microRNAs control respectively one or more specific biological phenomena and how the control is performed.
However, it has proven difficult to associate a specific biological phenomenon with a particular microRNA. One of the reasons lies in the few structural constraints that are exerted on the microRNA-mRNA duplexes. For example, in animals, unlike plants, these duplexes contain numerous mismatches, internal loops, and Wobble-type pairing (G:U). In addition, the number of consecutive pairings is very short (typically between 8 and 10 nucleotides located at the 5′ end of the microRNA (Lai et al., 2002)). Due to this weak interaction (including bulges and loops) between miRNA and its target (mRNA), thousands of possibilities can be predicted. Thus, the resulting list contains a majority of off-targets (i.e. background noise or false positives).
Identification of the targets, i.e. of the messenger RNAs, of the microRNAs by means of a simple molecular biology approach is therefore extremely difficult, or even impossible at this time. In an attempt to reply to such a problem, bioinformatics tools known, for example, under the following names: “Pictar” (Krek et al., 2005); “DIANA-microT” (Kiriakidou et al., 2004); “TargetScan” (Lewis et al., 2003); “(MiRanda” Enright et al., 2003), have been developed. These tools create binary relationships between microRNAs and their respective potential target recognition sequence(s). In other words, they associate an appropriate potential target recognition sequence list with each microRNA taken individually and, in the end, provide a gross list of these associations.
Although these tools have been useful in many ways, the existing tools have drawbacks. One drawback is that they result in a number of potential target recognition sequences for each microRNA that is much too great, which limits the exploitation of the results. Specifically, it is difficult to differentiate, in these large lists, the real (true) biological targets from the background noise.
Moreover, there is a great disparity in results between these tools for the same microRNA. For example, for the miR-15 microRNA, the “Pictar” tool provides a list of 746 possible targets, whereas, in “miRBase”, “TargetScan” and “MiRanda”, the list goes to 3918, 596 and 3456 potential target recognition sequences, respectively. By way of another example, for the miR-19 microRNA, the number of potential target recognition sequences provided is, respectively, 677, 5528, 527 and 1453. This further illustrates the abovementioned exploitation limits.
Therefore, it is desirable for improved methods of determining the biological functions associated with microRNA as well as their true target sequences. Additionally, it is desirable that these methods be transferable to other families of nucleic acids that are similarly situated.